{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 随机森林"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "\n",
    "X, y = datasets.make_moons(n_samples=500, noise=0.3, random_state=666)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.scatter(X[y==0,0], X[y==0,1])\n",
    "plt.scatter(X[y==1,0], X[y==1,1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 随机森林"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n                       criterion='gini', max_depth=None, max_features='auto',\n                       max_leaf_nodes=None, max_samples=None,\n                       min_impurity_decrease=0.0, min_impurity_split=None,\n                       min_samples_leaf=1, min_samples_split=2,\n                       min_weight_fraction_leaf=0.0, n_estimators=500,\n                       n_jobs=-1, oob_score=True, random_state=666, verbose=0,\n                       warm_start=False)"
     },
     "metadata": {},
     "execution_count": 4
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "rf_clf = RandomForestClassifier(n_estimators=500, oob_score=True, random_state=666, n_jobs=-1)\n",
    "rf_clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "0.892"
     },
     "metadata": {},
     "execution_count": 5
    }
   ],
   "source": [
    "rf_clf.oob_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "0.906"
     },
     "metadata": {},
     "execution_count": 6
    }
   ],
   "source": [
    "rf_clf2 = RandomForestClassifier(n_estimators=500, max_leaf_nodes=16, oob_score=True, random_state=666, n_jobs=-1)\n",
    "rf_clf2.fit(X, y)\n",
    "rf_clf2.oob_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "随机森林拥有决策树和BaggingClassifier的所有参数：）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extra-Trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "ExtraTreesClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n                     criterion='gini', max_depth=None, max_features='auto',\n                     max_leaf_nodes=None, max_samples=None,\n                     min_impurity_decrease=0.0, min_impurity_split=None,\n                     min_samples_leaf=1, min_samples_split=2,\n                     min_weight_fraction_leaf=0.0, n_estimators=500, n_jobs=-1,\n                     oob_score=True, random_state=666, verbose=0,\n                     warm_start=False)"
     },
     "metadata": {},
     "execution_count": 7
    }
   ],
   "source": [
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "\n",
    "et_clf = ExtraTreesClassifier(n_estimators=500, bootstrap=True, oob_score=True, random_state=666, n_jobs=-1)\n",
    "et_clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "0.892"
     },
     "metadata": {},
     "execution_count": 8
    }
   ],
   "source": [
    "et_clf.oob_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 集成学习解决回归问题"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import BaggingRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.ensemble import ExtraTreesRegressor"
   ]
  }
 ],
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   "display_name": "Python 3.6.8 64-bit ('anaconda': conda)",
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